Overview

Dataset statistics

Number of variables19
Number of observations43431
Missing cells150388
Missing cells (%)18.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 MiB
Average record size in memory152.0 B

Variable types

Numeric12
Categorical7

Alerts

from_date has a high cardinality: 20377 distinct valuesHigh cardinality
booking_created has a high cardinality: 39349 distinct valuesHigh cardinality
id is highly overall correlated with user_id and 1 other fieldsHigh correlation
user_id is highly overall correlated with id and 1 other fieldsHigh correlation
package_id is highly overall correlated with travel_type_id and 1 other fieldsHigh correlation
to_area_id is highly overall correlated with from_city_idHigh correlation
to_city_id is highly overall correlated with travel_type_idHigh correlation
to_date is highly overall correlated with id and 1 other fieldsHigh correlation
to_lat is highly overall correlated with from_city_idHigh correlation
to_long is highly overall correlated with from_city_idHigh correlation
travel_type_id is highly overall correlated with package_id and 1 other fieldsHigh correlation
from_city_id is highly overall correlated with package_id and 3 other fieldsHigh correlation
from_city_id is highly imbalanced (96.1%)Imbalance
mobile_site_booking is highly imbalanced (74.3%)Imbalance
Car_Cancellation is highly imbalanced (62.6%)Imbalance
package_id has 35881 (82.6%) missing valuesMissing
to_area_id has 9138 (21.0%) missing valuesMissing
from_city_id has 27086 (62.4%) missing valuesMissing
to_city_id has 41843 (96.3%) missing valuesMissing
to_date has 17890 (41.2%) missing valuesMissing
to_lat has 9138 (21.0%) missing valuesMissing
to_long has 9138 (21.0%) missing valuesMissing
to_date is highly skewed (γ1 = -50.22743775)Skewed
id is uniformly distributedUniform
booking_created is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-04-13 17:40:41.046479
Analysis finished2023-04-13 17:40:59.598416
Duration18.55 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct43431
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159206.47
Minimum132512
Maximum185941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:10:59.661367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum132512
5-th percentile135140
Q1145778
median159248
Q3172578.5
95-th percentile183262.5
Maximum185941
Range53429
Interquartile range (IQR)26800.5

Descriptive statistics

Standard deviation15442.386
Coefficient of variation (CV)0.09699597
Kurtosis-1.2030119
Mean159206.47
Median Absolute Deviation (MAD)13400
Skewness-0.00065524227
Sum6.9144964 × 109
Variance2.3846729 × 108
MonotonicityStrictly increasing
2023-04-13T23:10:59.784277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132512 1
 
< 0.1%
168178 1
 
< 0.1%
168168 1
 
< 0.1%
168169 1
 
< 0.1%
168170 1
 
< 0.1%
168171 1
 
< 0.1%
168172 1
 
< 0.1%
168174 1
 
< 0.1%
168175 1
 
< 0.1%
168177 1
 
< 0.1%
Other values (43421) 43421
> 99.9%
ValueCountFrequency (%)
132512 1
< 0.1%
132513 1
< 0.1%
132514 1
< 0.1%
132515 1
< 0.1%
132517 1
< 0.1%
132518 1
< 0.1%
132519 1
< 0.1%
132520 1
< 0.1%
132521 1
< 0.1%
132522 1
< 0.1%
ValueCountFrequency (%)
185941 1
< 0.1%
185940 1
< 0.1%
185939 1
< 0.1%
185938 1
< 0.1%
185937 1
< 0.1%
185936 1
< 0.1%
185935 1
< 0.1%
185933 1
< 0.1%
185932 1
< 0.1%
185931 1
< 0.1%

user_id
Real number (ℝ)

Distinct22267
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30739.198
Minimum16
Maximum48730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:10:59.888871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile5969
Q124614
median31627
Q339167
95-th percentile46185
Maximum48730
Range48714
Interquartile range (IQR)14553

Descriptive statistics

Standard deviation10996.477
Coefficient of variation (CV)0.35773466
Kurtosis0.40524291
Mean30739.198
Median Absolute Deviation (MAD)7265
Skewness-0.7458617
Sum1.3350341 × 109
Variance1.209225 × 108
MonotonicityNot monotonic
2023-04-13T23:10:59.996708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29648 471
 
1.1%
868 245
 
0.6%
27458 211
 
0.5%
32527 202
 
0.5%
34972 198
 
0.5%
33002 187
 
0.4%
29275 185
 
0.4%
32023 125
 
0.3%
30290 120
 
0.3%
38538 105
 
0.2%
Other values (22257) 41382
95.3%
ValueCountFrequency (%)
16 3
 
< 0.1%
21 1
 
< 0.1%
24 1
 
< 0.1%
35 4
 
< 0.1%
36 1
 
< 0.1%
37 1
 
< 0.1%
39 1
 
< 0.1%
41 2
 
< 0.1%
42 10
< 0.1%
47 3
 
< 0.1%
ValueCountFrequency (%)
48730 1
< 0.1%
48729 1
< 0.1%
48727 1
< 0.1%
48726 1
< 0.1%
48725 1
< 0.1%
48724 1
< 0.1%
48723 1
< 0.1%
48721 1
< 0.1%
48719 1
< 0.1%
48718 1
< 0.1%

vehicle_model_id
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.71723
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:11:00.099003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q112
median12
Q324
95-th percentile89
Maximum91
Range90
Interquartile range (IQR)12

Descriptive statistics

Standard deviation26.79825
Coefficient of variation (CV)1.0420349
Kurtosis0.99161464
Mean25.71723
Median Absolute Deviation (MAD)0
Skewness1.6685277
Sum1116925
Variance718.14622
MonotonicityNot monotonic
2023-04-13T23:11:00.180434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
12 31859
73.4%
85 2407
 
5.5%
89 2391
 
5.5%
65 1912
 
4.4%
28 1702
 
3.9%
24 1494
 
3.4%
87 565
 
1.3%
90 312
 
0.7%
23 297
 
0.7%
86 123
 
0.3%
Other values (17) 369
 
0.8%
ValueCountFrequency (%)
1 2
 
< 0.1%
10 104
 
0.2%
12 31859
73.4%
13 7
 
< 0.1%
14 1
 
< 0.1%
17 40
 
0.1%
23 297
 
0.7%
24 1494
 
3.4%
28 1702
 
3.9%
30 14
 
< 0.1%
ValueCountFrequency (%)
91 25
 
0.1%
90 312
 
0.7%
89 2391
5.5%
87 565
 
1.3%
86 123
 
0.3%
85 2407
5.5%
76 1
 
< 0.1%
75 1
 
< 0.1%
72 2
 
< 0.1%
70 1
 
< 0.1%

package_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.1%
Missing35881
Missing (%)82.6%
Infinite0
Infinite (%)0.0%
Mean2.0300662
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:11:00.259825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4617558
Coefficient of variation (CV)0.72005326
Kurtosis2.7266676
Mean2.0300662
Median Absolute Deviation (MAD)1
Skewness1.8508244
Sum15327
Variance2.13673
MonotonicityNot monotonic
2023-04-13T23:11:00.323606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 3503
 
8.1%
2 2651
 
6.1%
6 502
 
1.2%
4 412
 
0.9%
3 375
 
0.9%
7 101
 
0.2%
5 6
 
< 0.1%
(Missing) 35881
82.6%
ValueCountFrequency (%)
1 3503
8.1%
2 2651
6.1%
3 375
 
0.9%
4 412
 
0.9%
5 6
 
< 0.1%
6 502
 
1.2%
7 101
 
0.2%
ValueCountFrequency (%)
7 101
 
0.2%
6 502
 
1.2%
5 6
 
< 0.1%
4 412
 
0.9%
3 375
 
0.9%
2 2651
6.1%
1 3503
8.1%

travel_type_id
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.4 KiB
2
34292 
3
7550 
1
 
1589

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43431
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 34292
79.0%
3 7550
 
17.4%
1 1589
 
3.7%

Length

2023-04-13T23:11:00.405668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-13T23:11:00.495734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2 34292
79.0%
3 7550
 
17.4%
1 1589
 
3.7%

Most occurring characters

ValueCountFrequency (%)
2 34292
79.0%
3 7550
 
17.4%
1 1589
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 34292
79.0%
3 7550
 
17.4%
1 1589
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 43431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 34292
79.0%
3 7550
 
17.4%
1 1589
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 34292
79.0%
3 7550
 
17.4%
1 1589
 
3.7%

from_area_id
Real number (ℝ)

Distinct598
Distinct (%)1.4%
Missing88
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean714.54449
Minimum2
Maximum1403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:11:00.578533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile89
Q1393
median590
Q31089
95-th percentile1365
Maximum1403
Range1401
Interquartile range (IQR)696

Descriptive statistics

Standard deviation419.88355
Coefficient of variation (CV)0.58762408
Kurtosis-1.385828
Mean714.54449
Median Absolute Deviation (MAD)419
Skewness0.095403298
Sum30970502
Variance176302.2
MonotonicityNot monotonic
2023-04-13T23:11:00.678831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
393 3858
 
8.9%
571 1631
 
3.8%
293 1052
 
2.4%
585 911
 
2.1%
1010 768
 
1.8%
142 727
 
1.7%
83 719
 
1.7%
1384 628
 
1.4%
1096 542
 
1.2%
58 466
 
1.1%
Other values (588) 32041
73.8%
ValueCountFrequency (%)
2 31
 
0.1%
6 7
 
< 0.1%
15 6
 
< 0.1%
16 5
 
< 0.1%
17 2
 
< 0.1%
22 25
 
0.1%
24 26
 
0.1%
25 153
0.4%
34 33
 
0.1%
49 12
 
< 0.1%
ValueCountFrequency (%)
1403 1
 
< 0.1%
1401 8
 
< 0.1%
1399 86
0.2%
1398 17
 
< 0.1%
1397 2
 
< 0.1%
1396 3
 
< 0.1%
1395 32
 
0.1%
1394 9
 
< 0.1%
1393 37
 
0.1%
1391 97
0.2%

to_area_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct568
Distinct (%)1.7%
Missing9138
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean669.49092
Minimum2
Maximum1403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:11:00.793701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile136
Q1393
median541
Q31054
95-th percentile1384
Maximum1403
Range1401
Interquartile range (IQR)661

Descriptive statistics

Standard deviation400.63823
Coefficient of variation (CV)0.5984222
Kurtosis-1.1189618
Mean669.49092
Median Absolute Deviation (MAD)226
Skewness0.49370455
Sum22958852
Variance160510.99
MonotonicityNot monotonic
2023-04-13T23:11:00.894820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
393 8777
20.2%
585 2339
 
5.4%
1384 1237
 
2.8%
571 664
 
1.5%
293 555
 
1.3%
1010 480
 
1.1%
83 365
 
0.8%
168 338
 
0.8%
1371 332
 
0.8%
452 330
 
0.8%
Other values (558) 18876
43.5%
(Missing) 9138
21.0%
ValueCountFrequency (%)
2 4
 
< 0.1%
6 2
 
< 0.1%
15 5
 
< 0.1%
16 4
 
< 0.1%
17 1
 
< 0.1%
22 10
 
< 0.1%
24 7
 
< 0.1%
25 142
0.3%
34 15
 
< 0.1%
49 5
 
< 0.1%
ValueCountFrequency (%)
1403 1
 
< 0.1%
1401 2
 
< 0.1%
1399 74
 
0.2%
1398 11
 
< 0.1%
1395 55
 
0.1%
1394 8
 
< 0.1%
1393 185
0.4%
1391 37
 
0.1%
1390 114
0.3%
1389 17
 
< 0.1%

from_city_id
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing27086
Missing (%)62.4%
Memory size339.4 KiB
15.0
16233 
1.0
 
106
31.0
 
6

Length

Max length4
Median length4
Mean length3.9935148
Min length3

Characters and Unicode

Total characters65274
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15.0
2nd row15.0
3rd row1.0
4th row15.0
5th row1.0

Common Values

ValueCountFrequency (%)
15.0 16233
37.4%
1.0 106
 
0.2%
31.0 6
 
< 0.1%
(Missing) 27086
62.4%

Length

2023-04-13T23:11:00.994245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-13T23:11:01.081131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
15.0 16233
99.3%
1.0 106
 
0.6%
31.0 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 16345
25.0%
. 16345
25.0%
0 16345
25.0%
5 16233
24.9%
3 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48929
75.0%
Other Punctuation 16345
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16345
33.4%
0 16345
33.4%
5 16233
33.2%
3 6
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 16345
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16345
25.0%
. 16345
25.0%
0 16345
25.0%
5 16233
24.9%
3 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16345
25.0%
. 16345
25.0%
0 16345
25.0%
5 16233
24.9%
3 6
 
< 0.1%

to_city_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct116
Distinct (%)7.3%
Missing41843
Missing (%)96.3%
Infinite0
Infinite (%)0.0%
Mean68.537783
Minimum4
Maximum203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:11:01.169682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile24
Q132
median49
Q3108
95-th percentile161.65
Maximum203
Range199
Interquartile range (IQR)76

Descriptive statistics

Standard deviation49.880732
Coefficient of variation (CV)0.72778443
Kurtosis-0.29688118
Mean68.537783
Median Absolute Deviation (MAD)17
Skewness1.0276319
Sum108838
Variance2488.0874
MonotonicityNot monotonic
2023-04-13T23:11:01.272290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 475
 
1.1%
55 174
 
0.4%
29 116
 
0.3%
146 89
 
0.2%
108 64
 
0.1%
41 27
 
0.1%
58 26
 
0.1%
147 26
 
0.1%
131 26
 
0.1%
44 26
 
0.1%
Other values (106) 539
 
1.2%
(Missing) 41843
96.3%
ValueCountFrequency (%)
4 11
< 0.1%
5 2
 
< 0.1%
8 1
 
< 0.1%
10 3
 
< 0.1%
11 2
 
< 0.1%
12 6
 
< 0.1%
13 21
< 0.1%
14 5
 
< 0.1%
16 1
 
< 0.1%
17 5
 
< 0.1%
ValueCountFrequency (%)
203 4
 
< 0.1%
200 1
 
< 0.1%
198 5
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
192 5
< 0.1%
191 12
< 0.1%
190 8
< 0.1%
189 4
 
< 0.1%
187 1
 
< 0.1%

from_date
Categorical

Distinct20377
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Memory size339.4 KiB
10/12/2013 6:00
 
20
7/4/2013 22:15
 
20
9/8/2013 6:00
 
16
5/12/2013 7:30
 
15
9/16/2013 8:00
 
13
Other values (20372)
43347 

Length

Max length16
Median length15
Mean length14.477562
Min length13

Characters and Unicode

Total characters628775
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9095 ?
Unique (%)20.9%

Sample

1st row1/1/2013 2:00
2nd row1/1/2013 9:00
3rd row1/1/2013 3:30
4th row1/1/2013 5:45
5th row1/1/2013 9:00

Common Values

ValueCountFrequency (%)
10/12/2013 6:00 20
 
< 0.1%
7/4/2013 22:15 20
 
< 0.1%
9/8/2013 6:00 16
 
< 0.1%
5/12/2013 7:30 15
 
< 0.1%
9/16/2013 8:00 13
 
< 0.1%
5/12/2013 7:00 13
 
< 0.1%
5/19/2013 5:00 13
 
< 0.1%
9/5/2013 9:30 12
 
< 0.1%
10/9/2013 9:00 12
 
< 0.1%
9/2/2013 9:30 12
 
< 0.1%
Other values (20367) 43285
99.7%

Length

2023-04-13T23:11:01.374110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8:00 1150
 
1.3%
9:00 1123
 
1.3%
6:00 1078
 
1.2%
18:00 1023
 
1.2%
10:00 996
 
1.1%
9:30 994
 
1.1%
17:00 985
 
1.1%
8:30 920
 
1.1%
7:00 891
 
1.0%
7:30 848
 
1.0%
Other values (433) 76854
88.5%

Most occurring characters

ValueCountFrequency (%)
1 107437
17.1%
0 104488
16.6%
/ 86862
13.8%
2 74076
11.8%
3 71016
11.3%
43431
6.9%
: 43431
6.9%
5 24748
 
3.9%
4 17204
 
2.7%
8 15212
 
2.4%
Other values (3) 40870
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 455051
72.4%
Other Punctuation 130293
 
20.7%
Space Separator 43431
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 107437
23.6%
0 104488
23.0%
2 74076
16.3%
3 71016
15.6%
5 24748
 
5.4%
4 17204
 
3.8%
8 15212
 
3.3%
7 14233
 
3.1%
9 13896
 
3.1%
6 12741
 
2.8%
Other Punctuation
ValueCountFrequency (%)
/ 86862
66.7%
: 43431
33.3%
Space Separator
ValueCountFrequency (%)
43431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 628775
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 107437
17.1%
0 104488
16.6%
/ 86862
13.8%
2 74076
11.8%
3 71016
11.3%
43431
6.9%
: 43431
6.9%
5 24748
 
3.9%
4 17204
 
2.7%
8 15212
 
2.4%
Other values (3) 40870
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 628775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 107437
17.1%
0 104488
16.6%
/ 86862
13.8%
2 74076
11.8%
3 71016
11.3%
43431
6.9%
: 43431
6.9%
5 24748
 
3.9%
4 17204
 
2.7%
8 15212
 
2.4%
Other values (3) 40870
 
6.5%

to_date
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct22151
Distinct (%)86.7%
Missing17890
Missing (%)41.2%
Infinite0
Infinite (%)0.0%
Mean41507.975
Minimum25569.021
Maximum41678.276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:11:01.722253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25569.021
5-th percentile41403
Q141480.521
median41517.762
Q341558.915
95-th percentile41594.225
Maximum41678.276
Range16109.255
Interquartile range (IQR)78.39352

Descriptive statistics

Standard deviation305.14494
Coefficient of variation (CV)0.0073514775
Kurtosis2620.6252
Mean41507.975
Median Absolute Deviation (MAD)39.432
Skewness-50.227438
Sum1.0601552 × 109
Variance93113.437
MonotonicityNot monotonic
2023-04-13T23:11:01.832537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41427 38
 
0.1%
41406 34
 
0.1%
41434 34
 
0.1%
41420 32
 
0.1%
41426 31
 
0.1%
41413 29
 
0.1%
41433 26
 
0.1%
41447 25
 
0.1%
41405 24
 
0.1%
41440 24
 
0.1%
Other values (22141) 25244
58.1%
(Missing) 17890
41.2%
ValueCountFrequency (%)
25569.02083 2
< 0.1%
25569.22917 1
< 0.1%
25569.29167 1
< 0.1%
25569.375 1
< 0.1%
25569.38542 1
< 0.1%
25569.41667 1
< 0.1%
25569.66667 1
< 0.1%
25569.875 1
< 0.1%
41275 1
< 0.1%
41275.40625 1
< 0.1%
ValueCountFrequency (%)
41678.27568 1
< 0.1%
41677.63016 1
< 0.1%
41638 1
< 0.1%
41634.99931 1
< 0.1%
41623.38366 1
< 0.1%
41623.24115 1
< 0.1%
41619.97228 1
< 0.1%
41619.01017 1
< 0.1%
41618.93374 1
< 0.1%
41618.21785 1
< 0.1%

online_booking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.4 KiB
0
28161 
1
15270 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28161
64.8%
1 15270
35.2%

Length

2023-04-13T23:11:01.929706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-13T23:11:02.018458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28161
64.8%
1 15270
35.2%

Most occurring characters

ValueCountFrequency (%)
0 28161
64.8%
1 15270
35.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28161
64.8%
1 15270
35.2%

Most occurring scripts

ValueCountFrequency (%)
Common 43431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28161
64.8%
1 15270
35.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28161
64.8%
1 15270
35.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.4 KiB
0
41553 
1
 
1878

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 41553
95.7%
1 1878
 
4.3%

Length

2023-04-13T23:11:02.089036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-13T23:11:02.169913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 41553
95.7%
1 1878
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 41553
95.7%
1 1878
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41553
95.7%
1 1878
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 43431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41553
95.7%
1 1878
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41553
95.7%
1 1878
 
4.3%

booking_created
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct39349
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size339.4 KiB
10/31/2013 10:30
 
18
10/31/2013 9:58
 
17
10/3/2013 14:13
 
16
8/1/2013 7:24
 
15
8/31/2013 9:51
 
14
Other values (39344)
43351 

Length

Max length16
Median length15
Mean length14.701296
Min length13

Characters and Unicode

Total characters638492
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36714 ?
Unique (%)84.5%

Sample

1st row1/1/2013 1:39
2nd row1/1/2013 2:25
3rd row1/1/2013 3:08
4th row1/1/2013 4:39
5th row1/1/2013 7:53

Common Values

ValueCountFrequency (%)
10/31/2013 10:30 18
 
< 0.1%
10/31/2013 9:58 17
 
< 0.1%
10/3/2013 14:13 16
 
< 0.1%
8/1/2013 7:24 15
 
< 0.1%
8/31/2013 9:51 14
 
< 0.1%
10/31/2013 12:17 13
 
< 0.1%
8/1/2013 13:47 13
 
< 0.1%
8/30/2013 17:54 13
 
< 0.1%
10/31/2013 12:24 13
 
< 0.1%
10/31/2013 9:43 13
 
< 0.1%
Other values (39339) 43286
99.7%

Length

2023-04-13T23:11:02.241784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10/31/2013 515
 
0.6%
8/30/2013 363
 
0.4%
7/6/2013 339
 
0.4%
9/27/2013 305
 
0.4%
8/1/2013 287
 
0.3%
8/31/2013 249
 
0.3%
6/1/2013 244
 
0.3%
8/2/2013 238
 
0.3%
9/30/2013 236
 
0.3%
7/12/2013 228
 
0.3%
Other values (1733) 83858
96.5%

Most occurring characters

ValueCountFrequency (%)
1 121028
19.0%
2 89831
14.1%
/ 86862
13.6%
0 70987
11.1%
3 69371
10.9%
43431
 
6.8%
: 43431
 
6.8%
5 22577
 
3.5%
4 21439
 
3.4%
9 18291
 
2.9%
Other values (3) 51244
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 464768
72.8%
Other Punctuation 130293
 
20.4%
Space Separator 43431
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 121028
26.0%
2 89831
19.3%
0 70987
15.3%
3 69371
14.9%
5 22577
 
4.9%
4 21439
 
4.6%
9 18291
 
3.9%
8 18178
 
3.9%
7 17237
 
3.7%
6 15829
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/ 86862
66.7%
: 43431
33.3%
Space Separator
ValueCountFrequency (%)
43431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 638492
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 121028
19.0%
2 89831
14.1%
/ 86862
13.6%
0 70987
11.1%
3 69371
10.9%
43431
 
6.8%
: 43431
 
6.8%
5 22577
 
3.5%
4 21439
 
3.4%
9 18291
 
2.9%
Other values (3) 51244
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 638492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 121028
19.0%
2 89831
14.1%
/ 86862
13.6%
0 70987
11.1%
3 69371
10.9%
43431
 
6.8%
: 43431
 
6.8%
5 22577
 
3.5%
4 21439
 
3.4%
9 18291
 
2.9%
Other values (3) 51244
8.0%

from_lat
Real number (ℝ)

Distinct466
Distinct (%)1.1%
Missing93
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean12.982461
Minimum12.77663
Maximum13.366072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:11:02.331646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.77663
5-th percentile12.869805
Q112.92645
median12.968887
Q313.00775
95-th percentile13.19956
Maximum13.366072
Range0.589442
Interquartile range (IQR)0.0813

Descriptive statistics

Standard deviation0.085932527
Coefficient of variation (CV)0.0066191247
Kurtosis1.3719346
Mean12.982461
Median Absolute Deviation (MAD)0.041207
Skewness1.2104129
Sum562633.89
Variance0.0073843992
MonotonicityNot monotonic
2023-04-13T23:11:02.433176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.19956 3858
 
8.9%
12.95185 1631
 
3.8%
12.97677 1080
 
2.5%
12.849482 1052
 
2.4%
12.92415 960
 
2.2%
13.02853 928
 
2.1%
12.96691 776
 
1.8%
12.91281 732
 
1.7%
12.96519 726
 
1.7%
12.9744 468
 
1.1%
Other values (456) 31127
71.7%
ValueCountFrequency (%)
12.77663 13
 
< 0.1%
12.78091 12
 
< 0.1%
12.78665 11
 
< 0.1%
12.79665 10
 
< 0.1%
12.80257 26
 
0.1%
12.81575 124
0.3%
12.824606 3
 
< 0.1%
12.827912 28
 
0.1%
12.832201 4
 
< 0.1%
12.833854 20
 
< 0.1%
ValueCountFrequency (%)
13.366072 5
 
< 0.1%
13.24373 22
 
0.1%
13.206811 9
 
< 0.1%
13.19966 37
 
0.1%
13.19956 3858
8.9%
13.175103 1
 
< 0.1%
13.17256 1
 
< 0.1%
13.13785 40
 
0.1%
13.13243 17
 
< 0.1%
13.13128 1
 
< 0.1%

from_long
Real number (ℝ)

Distinct462
Distinct (%)1.1%
Missing93
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean77.636255
Minimum77.38693
Maximum77.78642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:11:02.545346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum77.38693
5-th percentile77.54481
Q177.593661
median77.63575
Q377.6889
95-th percentile77.71932
Maximum77.78642
Range0.39949
Interquartile range (IQR)0.095239

Descriptive statistics

Standard deviation0.059390976
Coefficient of variation (CV)0.00076499023
Kurtosis-0.60829059
Mean77.636255
Median Absolute Deviation (MAD)0.046623
Skewness-0.15391031
Sum3364600
Variance0.003527288
MonotonicityNot monotonic
2023-04-13T23:11:02.661809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.70688 3895
 
9.0%
77.69642 1631
 
3.8%
77.5727 1080
 
2.5%
77.663187 1052
 
2.4%
77.67229 960
 
2.2%
77.54625 928
 
2.1%
77.74935 776
 
1.8%
77.60923 732
 
1.7%
77.71932 726
 
1.7%
77.69183 468
 
1.1%
Other values (452) 31090
71.6%
ValueCountFrequency (%)
77.38693 10
 
< 0.1%
77.38845 7
 
< 0.1%
77.400913 20
< 0.1%
77.442232 5
 
< 0.1%
77.45876 2
 
< 0.1%
77.461074 2
 
< 0.1%
77.473672 33
0.1%
77.474893 1
 
< 0.1%
77.47725 40
0.1%
77.480214 30
0.1%
ValueCountFrequency (%)
77.78642 30
 
0.1%
77.77131 12
 
< 0.1%
77.761764 12
 
< 0.1%
77.750528 47
 
0.1%
77.74935 776
1.8%
77.747099 135
 
0.3%
77.74657 69
 
0.2%
77.737731 9
 
< 0.1%
77.736123 18
 
< 0.1%
77.735245 242
 
0.6%

to_lat
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct450
Distinct (%)1.3%
Missing9138
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean13.026648
Minimum12.77663
Maximum13.366072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:11:02.776548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.77663
5-th percentile12.88963
Q112.95185
median12.98275
Q313.19956
95-th percentile13.19956
Maximum13.366072
Range0.589442
Interquartile range (IQR)0.24771

Descriptive statistics

Standard deviation0.11348743
Coefficient of variation (CV)0.0087119438
Kurtosis-1.032068
Mean13.026648
Median Absolute Deviation (MAD)0.05253
Skewness0.54453592
Sum446722.86
Variance0.012879397
MonotonicityNot monotonic
2023-04-13T23:11:02.870839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.19956 8777
20.2%
12.97677 2573
 
5.9%
13.02853 1531
 
3.5%
12.95185 664
 
1.5%
12.849482 555
 
1.3%
12.96691 484
 
1.1%
12.92415 447
 
1.0%
12.91873 377
 
0.9%
12.99313 338
 
0.8%
13.000418 332
 
0.8%
Other values (440) 18215
41.9%
(Missing) 9138
21.0%
ValueCountFrequency (%)
12.77663 17
 
< 0.1%
12.78091 5
 
< 0.1%
12.78665 12
 
< 0.1%
12.79665 4
 
< 0.1%
12.80257 27
 
0.1%
12.81575 147
0.3%
12.824606 1
 
< 0.1%
12.827912 11
 
< 0.1%
12.832201 5
 
< 0.1%
12.833854 18
 
< 0.1%
ValueCountFrequency (%)
13.366072 16
 
< 0.1%
13.24373 25
 
0.1%
13.206811 3
 
< 0.1%
13.19966 185
 
0.4%
13.19956 8777
20.2%
13.175103 1
 
< 0.1%
13.17318 1
 
< 0.1%
13.17256 2
 
< 0.1%
13.13785 24
 
0.1%
13.13243 12
 
< 0.1%

to_long
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct447
Distinct (%)1.3%
Missing9138
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean77.640595
Minimum77.38693
Maximum77.78642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.4 KiB
2023-04-13T23:11:02.991593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum77.38693
5-th percentile77.54625
Q177.58203
median77.64503
Q377.70688
95-th percentile77.71662
Maximum77.78642
Range0.39949
Interquartile range (IQR)0.12485

Descriptive statistics

Standard deviation0.064045299
Coefficient of variation (CV)0.00082489448
Kurtosis-1.0068015
Mean77.640595
Median Absolute Deviation (MAD)0.06185
Skewness-0.26540506
Sum2662528.9
Variance0.0041018003
MonotonicityNot monotonic
2023-04-13T23:11:03.096691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.70688 8962
20.6%
77.5727 2573
 
5.9%
77.54625 1531
 
3.5%
77.69642 664
 
1.5%
77.663187 555
 
1.3%
77.74935 484
 
1.1%
77.67229 447
 
1.0%
77.61494 377
 
0.9%
77.59828 338
 
0.8%
77.674835 332
 
0.8%
Other values (437) 18030
41.5%
(Missing) 9138
21.0%
ValueCountFrequency (%)
77.38693 4
 
< 0.1%
77.38845 7
 
< 0.1%
77.400913 18
 
< 0.1%
77.442232 10
 
< 0.1%
77.45876 2
 
< 0.1%
77.461074 2
 
< 0.1%
77.473672 52
0.1%
77.47725 24
 
0.1%
77.480214 28
 
0.1%
77.48279 82
0.2%
ValueCountFrequency (%)
77.78642 19
 
< 0.1%
77.77131 5
 
< 0.1%
77.761764 18
 
< 0.1%
77.750528 34
 
0.1%
77.74935 484
1.1%
77.747099 47
 
0.1%
77.74657 19
 
< 0.1%
77.74481 51
 
0.1%
77.737731 3
 
< 0.1%
77.736123 6
 
< 0.1%

Car_Cancellation
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.4 KiB
0
40299 
1
 
3132

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 40299
92.8%
1 3132
 
7.2%

Length

2023-04-13T23:11:03.195838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-13T23:11:03.277403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 40299
92.8%
1 3132
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 40299
92.8%
1 3132
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40299
92.8%
1 3132
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common 43431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40299
92.8%
1 3132
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40299
92.8%
1 3132
 
7.2%

Interactions

2023-04-13T23:10:57.649844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:43.619376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:45.032485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:46.239187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:47.427292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.634226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:49.861138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:51.046984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:52.305403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:53.587626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:54.817969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:56.258476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:57.763638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:43.749761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:45.135982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:46.335045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:47.519419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.736822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:49.976861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:51.151488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:52.402617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:53.693507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:54.925856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:56.358516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:57.863084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:43.841390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:45.233861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:46.431741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:47.606612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.827762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:50.060975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:51.243475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:52.504588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:53.786465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:55.037432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:56.688612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:57.951959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:43.938338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:45.325224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:46.508242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:47.703567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.930690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:50.167095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:51.346902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:52.623209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:53.876758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:55.142600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:56.783363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:58.014952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:44.038343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:45.427156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:46.622880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:47.787492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:49.029639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:50.248404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:51.408987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:52.722934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:53.976724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:55.256326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:56.871802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:58.135777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:44.150356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:45.529023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:46.723087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.007663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:49.138462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:50.344196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:51.527153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:52.838162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:54.077037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:55.378253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:56.984925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:58.236487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:44.237405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:45.621949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:46.812871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.113660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:49.228229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:50.451780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:51.746200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:52.942508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:54.177096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:55.487702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:57.089251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:58.293513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:44.347179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:45.722883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:46.919933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.179947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:49.342143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:50.527389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:51.856690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:53.054744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:54.285062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:55.606647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:57.164021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:58.413674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:44.454716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:45.824850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:47.031738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.270498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:49.444981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:50.636894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:51.959539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:53.169320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:54.385925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:55.735272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:57.266040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:58.514177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:44.558610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:45.924865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:47.126830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.377126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:49.553808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:50.727227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:52.053528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:53.271218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:54.493849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:55.852425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:57.359556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:58.629107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:44.672311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:46.035336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:47.233484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.477856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:49.663046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:50.846863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:52.156668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:53.383891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:54.601007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:55.988700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:57.449132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:58.742755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:44.899603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:46.125187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:47.327113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:48.550046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:49.763021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:50.945868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:52.222608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:53.476903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:54.707442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:56.129441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T23:10:57.558948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-13T23:11:03.355345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
iduser_idvehicle_model_idpackage_idfrom_area_idto_area_idto_city_idto_datefrom_latfrom_longto_latto_longtravel_type_idfrom_city_idonline_bookingmobile_site_bookingCar_Cancellation
id1.0000.6860.0360.3280.0230.025-0.0270.9950.0360.0320.0270.0310.0350.2480.1260.1000.215
user_id0.6861.0000.0590.2250.0190.011-0.0380.5580.0410.0100.0490.0390.0540.0750.1310.0660.149
vehicle_model_id0.0360.0591.0000.160-0.023-0.0430.0030.0170.0510.0220.0530.0610.2180.0940.0230.0230.080
package_id0.3280.2250.1601.000-0.000NaNNaN0.347-0.0130.030NaNNaN1.0001.0000.1320.0590.126
from_area_id0.0230.019-0.023-0.0001.0000.0120.020-0.002-0.003-0.1110.0020.0070.0520.0330.0890.0370.046
to_area_id0.0250.011-0.043NaN0.0121.000NaN0.0010.0170.020-0.159-0.3300.0001.0000.1150.0320.137
to_city_id-0.027-0.0380.003NaN0.020NaN1.000-0.035-0.011-0.002NaNNaN1.0000.0800.0000.0970.000
to_date0.9950.5580.0170.347-0.0020.001-0.0351.0000.0300.0090.0040.0130.0750.1950.0100.0000.000
from_lat0.0360.0410.051-0.013-0.0030.017-0.0110.0301.0000.110-0.122-0.0330.0890.0410.0880.0520.083
from_long0.0320.0100.0220.030-0.1110.020-0.0020.0090.1101.000-0.0570.0280.0700.0270.1030.0310.088
to_lat0.0270.0490.053NaN0.002-0.159NaN0.004-0.122-0.0571.0000.4070.0001.0000.0980.0150.147
to_long0.0310.0390.061NaN0.007-0.330NaN0.013-0.0330.0280.4071.0000.0001.0000.0890.0080.136
travel_type_id0.0350.0540.2181.0000.0520.0001.0000.0750.0890.0700.0000.0001.0000.1920.0700.0460.069
from_city_id0.2480.0750.0941.0000.0331.0000.0800.1950.0410.0271.0001.0000.1921.0000.0880.0200.026
online_booking0.1260.1310.0230.1320.0890.1150.0000.0100.0880.1030.0980.0890.0700.0881.0000.1560.149
mobile_site_booking0.1000.0660.0230.0590.0370.0320.0970.0000.0520.0310.0150.0080.0460.0200.1561.0000.067
Car_Cancellation0.2150.1490.0800.1260.0460.1370.0000.0000.0830.0880.1470.1360.0690.0260.1490.0671.000

Missing values

2023-04-13T23:10:58.900326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-13T23:10:59.150312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-13T23:10:59.430402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

iduser_idvehicle_model_idpackage_idtravel_type_idfrom_area_idto_area_idfrom_city_idto_city_idfrom_dateto_dateonline_bookingmobile_site_bookingbooking_createdfrom_latfrom_longto_latto_longCar_Cancellation
01325122217728NaN283.0448.0NaNNaN1/1/2013 2:00NaN001/1/2013 1:3912.92415077.67229012.92732077.6357500
11325132141312NaN21010.0540.0NaNNaN1/1/2013 9:00NaN001/1/2013 2:2512.96691077.74935012.92768077.6266400
21325142217812NaN21301.01034.0NaNNaN1/1/2013 3:30NaN001/1/2013 3:0812.93722277.62691513.04792677.5977660
31325151303412NaN2768.0398.0NaNNaN1/1/2013 5:45NaN001/1/2013 4:3912.98999077.55332012.97143077.6391400
41325172218012NaN21365.0849.0NaNNaN1/1/2013 9:00NaN001/1/2013 7:5312.84565377.67792512.95434077.6007200
51325181771212NaN21021.01323.0NaNNaN1/1/2013 22:30NaN001/1/2013 8:0113.02853077.54625012.86980577.6532110
61325192217212NaN1571.0NaN15.0108.01/1/2013 9:4541275.40625001/1/2013 9:2112.95185077.696420NaNNaN0
71325202218112NaN21192.0832.0NaNNaN1/1/2013 11:00NaN001/1/2013 9:3912.97677077.57270012.88019077.6455800
813252122182652.03448.0NaNNaNNaN1/1/2013 16:00NaN001/1/2013 9:4412.92732077.635750NaNNaN0
91325222218412NaN2516.0376.0NaNNaN1/1/2013 11:00NaN001/1/2013 9:4913.00560077.65799012.90245077.6608100
iduser_idvehicle_model_idpackage_idtravel_type_idfrom_area_idto_area_idfrom_city_idto_city_idfrom_dateto_dateonline_bookingmobile_site_bookingbooking_createdfrom_latfrom_longto_latto_longCar_Cancellation
434211859314872312NaN21371.01181.015.0NaN11/25/2013 9:4541603.431180011/24/2013 14:1013.00041877.67483512.97896077.673450
434221859324872412NaN2393.0269.015.0NaN11/24/2013 21:1541602.983450011/24/2013 14:1213.19956077.70688012.97440077.691831
434231859334872524NaN2585.0339.015.0NaN11/24/2013 16:0041602.709130111/24/2013 14:1512.97677077.57270012.91028077.645120
4342418593548726122.03515.0NaN15.0NaN11/24/2013 15:1541603.010420011/24/2013 14:2312.97896077.673450NaNNaN0
434251859363022012NaN2585.01226.015.0NaN11/25/2013 0:0041603.033360111/24/2013 14:2412.97677077.57270013.01508077.677960
434261859371436412NaN21147.0452.015.0NaN11/24/2013 18:0041602.820201011/24/2013 14:2513.03064077.64910012.95278077.590880
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